AI turns decades of cybersecurity upside down

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Organizations are embracing AI en masse to accelerate processes. Without the right security, however, this innovation can quickly become a loose cannon. How do you ensure that the AI your organization uses runs securely and isn’t compromised? We’ll discuss this in an in-depth roundtable discussion with experts from ManageEngine, Nutanix, Okta, Thales, TrendAI, Veeam, and Zscaler.

For IT professionals and decision-makers, AI security can still be a bit unclear. Everyone is talking about it, but the concrete details remain abstract. To clarify exactly what it entails, we’ve brought together a diverse group of security experts across the tech stack. What does AI security really mean? Is it about full-stack security or rather specific runtime controls? And now that the market is realizing that AI comes with a hefty price tag, the question arises: how do we keep protection affordable and practical for the entire workforce?

It’s high time to take stock of the gap between the theory of the models and the stubborn reality on the ground. We’ll do that in a two-part series, of which this is the first installment.

Design flaw in the architecture

Securing AI applications is deeply intertwined with our fundamental approach to IT architecture and access management. Albert Kramer of Zscaler gets straight to the point on this issue, stating that AI poses a massive threat to what the industry has built up over the past decades. “What AI promises is the highly efficient use of data. But we’ve skipped a few crucial steps in granting that access,” Kramer explains. “In the world of zero trust, we actually start with no access at all. We decide what to grant access to based on the user’s identity or intent. Right now, we’re struggling enormously with that concept because we’re effectively giving AI agents unlimited access. This causes countless compliance issues and data breaches. It literally turns the past 25 to 30 years of…

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